Foundations of AI Product Thinking

This unit introduces core concepts and mindsets required to move from AI demonstrations to impactful, business-aligned products. Students will learn fundamental machine learning concepts, the MDLC and nine-step framework, and how to design experiments, evaluate risks (explainability and model drift), and align AI solutions with business goals to prepare for hands-on product development in subsequent units.

Learning Objectives

• Analyze the role of AI and machine learning (including generative AI) in business problem-solving by producing a comparative brief that maps at least two AI use cases to specific business goals and metrics.
• Apply the nine-step framework and the Model Development Life Cycle (MDLC) to design a research and experimentation plan that specifies hypotheses, success metrics, data needs, and timelines for an AI product concept.
• Demonstrate an experimentation mindset by creating, executing, and documenting at least two small-scale experiments or prototypes that measure model performance, explainability, and initial business impact.
• Evaluate ethical, operational, and performance risks—such as explainability challenges and model drift—and propose prioritized mitigation strategies aligned with organizational objectives and stakeholder needs.